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Artificial Intelligence in Pathology: Advancing Large Models for Scalable Applications

Zhiping Xiao*, Bin Feng, Junwei Yang, Gongbo Sun, Yuxi Shen, Shengyuan Xu, Lina Yang, Hanwen Xu, Ming Zhang, Sheng Wang*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Downloads (CityUHK Scholars)

Abstract

The rapid development of artificial intelligence (AI) has had a significant impact on medical research, introducing new possibilities for pathology studies. There is a recent trend of applying large-scale AI models to many fields, and this trend has given rise to the pathology foundation models and pathology ensemble models. Large models in pathology are not standalone innovations; they build upon a legacy where AI has consistently played a vital role in pathology studies long before their advent. Numerous pathology datasets and AI models have been developed to support advancements in the field, with these combined efforts paving the way for the emergence of large models in pathology. AI greatly enhances pathology studies, yet its widespread use in sensitive applications also raises significant ethical concerns, including privacy risks. In this review, we summarize the datasets and models that are useful to pathology studies, with a particular focus on how they illuminate the path toward large-scale applications. © 2025 by the author(s).
Original languageEnglish
Pages (from-to)149-171
Number of pages23
JournalAnnual Review of Biomedical Data Science
Volume8
DOIs
Publication statusPublished - Aug 2025
Externally publishedYes

Research Keywords

  • artificial intelligence
  • deep learning
  • ensemble models
  • foundation models
  • machine learning
  • medical datasets
  • pathology

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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